The uform3-image-text-english-small UForm model is a tiny vision and English language encoder, mapping them into a shared vector space.
This model produces up to 256-dimensional embeddings and is made of:
- Text encoder: 4-layer BERT for up to 64 input tokens.
- Visual encoder: ViT-S/16 for images of 224 x 224 resolution.
Unlike most CLIP-like multomodal models, this model shares 2 layers between the text and visual encoder to allow for more data- and parameter-efficient training. Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code. If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub. For more details on running the model, check out the UForm GitHub repository.
Evaluation
For zero-shot ImageNet classification the model achieves Top-1 accuracy of 36.1% and Top-5 of 60.8%. On text-to-image retrieval it reaches 86% Recall@10 for Flickr:
| Dataset | Recall@1 | Recall@5 | Recall@10 |
|---|---|---|---|
| Zero-Shot Flickr | 0.565 | 0.790 | 0.860 |
| Zero-Shot MS-COCO | 0.281 | 0.525 | 0.645 |
Installation
pip install "uform[torch,onnx]"
Usage
To load the model:
from uform import get_model, Modality
import requests
from io import BytesIO
from PIL import Image
model_name = 'unum-cloud/uform3-image-text-english-small'
modalities = [Modality.TEXT_ENCODER, Modality.IMAGE_ENCODER]
processors, models = get_model(model_name, modalities=modalities)
model_text = models[Modality.TEXT_ENCODER]
model_image = models[Modality.IMAGE_ENCODER]
processor_text = processors[Modality.TEXT_ENCODER]
processor_image = processors[Modality.IMAGE_ENCODER]
To encode the content:
text = 'a cityscape bathed in the warm glow of the sun, with varied architecture and a towering, snow-capped mountain rising majestically in the background'
image_url = 'https://media-cdn.tripadvisor.com/media/photo-s/1b/28/6b/53/lovely-armenia.jpg'
image_url = Image.open(BytesIO(requests.get(image_url).content))
image_data = processor_image(image)
text_data = processor_text(text)
image_features, image_embedding = model_image.encode(image_data, return_features=True)
text_features, text_embedding = model_text.encode(text_data, return_features=True)